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Abstract

Output capacitors are among the most critical components in power electronic converters, as their degradation directly affects system stability and reliability. A key indicator of capacitor health is the Equivalent Series Resistance (ESR), whose progressive increase is strongly correlated with aging and imminent failure. This paper presents a real-time technique for estimating the ESR of output capacitors in boost converters, with the aim of enabling early fault prediction and condition-based maintenance. The proposed method leverages online parameter estimation to extract ESR values without interrupting converter operation. The estimation algorithm has been implemented on a low-cost STM32 Nucleo platform, demonstrating both computational efficiency and suitability for embedded applications. Experimental validation confirms that the approach provides accurate ESR tracking under varying load and operating conditions, allowing timely detection of abnormal capacitor behavior and preventing unexpected system failures.